Identifying birds by species and sex is essential for recording game harvests, monitoring the status of game resources, and ensuring compliance with established hunting regulations. However, existing datasets and digital applications primarily focus on species identification and do not account for the sex of individual birds, which limits their applicability to game management. This paper presents GameWaterbird-41, a specialized dataset designed for the joint classification of species and sex in game waterfowl and shorebird species classified as game species. Compiled from images obtained from the iNaturalist platform, the dataset contains 18,851 images representing 38 bird species and is organized into 41 classes, with sex-specific classes defined for species exhibiting pronounced sexual dimorphism. Modern computer vision models were comparatively evaluated under zero-shot classification, linear probing, and full fine-tuning settings. The evaluated architectures included convolutional neural networks and vision transformers, encompassing both general-purpose models and models pretrained on biological images. The results demonstrate that full fine-tuning on the target data yields the highest classification performance. The developed dataset and comparative evaluation results can be used to improve game harvest recording and provide information support for game resource management.